Goals for this file

Use raw fastq and generate the quality plots to asses the quality of reads
Filter and trim out bad sequences and bases from our sequencing files
Write out fastq files with high quality sequences
Evaluate the quality from our filter and trim.
Infer errors on forward and reverse reads individually
Identified ASVs on forward and reverse reads separately using the error model.
Merge forward and reverse ASVs into “contigous ASVs”.
Generate ASV count table. (otu_table input for phyloseq.).

Output that we need:

ASV count table: otu_table
Sample information: sample_table track the reads lost throughout DADA2 workflow.

Before you start

Set my seed

# Any number can be chose
set.seed(567890)

Load Libraries

#Effecient package loading with pacman
pacman::p_load(tidyverse, devtools, dada2, phyloseq, patchwork, DT,
               install = FALSE)

Load Data

#Set the raw fastq path to the raw sequencing files
#Path to the fastq files
raw_fastqs_path <- "data/01_DADA2/01_raw_gzipped_fastqs/2011"
raw_fastqs_path

## [1] "data/01_DADA2/01_raw_gzipped_fastqs/2011"

#What files are in this path (Intuition check)
list.files(raw_fastqs_path)

##  [1] "SRR17060822_1.fastq.gz" "SRR17060822_2.fastq.gz" "SRR17060823_1.fastq.gz"
##  [4] "SRR17060823_2.fastq.gz" "SRR17060825_1.fastq.gz" "SRR17060825_2.fastq.gz"
##  [7] "SRR17060826_1.fastq.gz" "SRR17060826_2.fastq.gz" "SRR17060827_1.fastq.gz"
## [10] "SRR17060827_2.fastq.gz" "SRR17060828_1.fastq.gz" "SRR17060828_2.fastq.gz"
## [13] "SRR17060829_1.fastq.gz" "SRR17060829_2.fastq.gz" "SRR17060839_1.fastq.gz"
## [16] "SRR17060839_2.fastq.gz" "SRR17060840_1.fastq.gz" "SRR17060840_2.fastq.gz"
## [19] "SRR17060841_1.fastq.gz" "SRR17060841_2.fastq.gz" "SRR17060842_1.fastq.gz"
## [22] "SRR17060842_2.fastq.gz" "SRR17060843_1.fastq.gz" "SRR17060843_2.fastq.gz"
## [25] "SRR17060844_1.fastq.gz" "SRR17060844_2.fastq.gz" "SRR17060845_1.fastq.gz"
## [28] "SRR17060845_2.fastq.gz"

#How many files are there?
str(list.files(raw_fastqs_path))

##  chr [1:28] "SRR17060822_1.fastq.gz" "SRR17060822_2.fastq.gz" ...

#Create a vector of forward reads
forward_reads <- list.files(raw_fastqs_path, pattern = "_1.fastq.gz", full.names = TRUE)
#Intuition check
head(forward_reads)

## [1] "data/01_DADA2/01_raw_gzipped_fastqs/2011/SRR17060822_1.fastq.gz"
## [2] "data/01_DADA2/01_raw_gzipped_fastqs/2011/SRR17060823_1.fastq.gz"
## [3] "data/01_DADA2/01_raw_gzipped_fastqs/2011/SRR17060825_1.fastq.gz"
## [4] "data/01_DADA2/01_raw_gzipped_fastqs/2011/SRR17060826_1.fastq.gz"
## [5] "data/01_DADA2/01_raw_gzipped_fastqs/2011/SRR17060827_1.fastq.gz"
## [6] "data/01_DADA2/01_raw_gzipped_fastqs/2011/SRR17060828_1.fastq.gz"

#Create a vector of reverse reads
reverse_reads <-list.files(raw_fastqs_path, pattern = "_2.fastq.gz", full.names = TRUE)
#Intuition check
head(reverse_reads)

## [1] "data/01_DADA2/01_raw_gzipped_fastqs/2011/SRR17060822_2.fastq.gz"
## [2] "data/01_DADA2/01_raw_gzipped_fastqs/2011/SRR17060823_2.fastq.gz"
## [3] "data/01_DADA2/01_raw_gzipped_fastqs/2011/SRR17060825_2.fastq.gz"
## [4] "data/01_DADA2/01_raw_gzipped_fastqs/2011/SRR17060826_2.fastq.gz"
## [5] "data/01_DADA2/01_raw_gzipped_fastqs/2011/SRR17060827_2.fastq.gz"
## [6] "data/01_DADA2/01_raw_gzipped_fastqs/2011/SRR17060828_2.fastq.gz"

Raw Quality plots

# Randomly select 12 samples from dataset to evaluate 
# Selecting 12 is typically better than 2 (like we did in class for efficiency)
random_samples <- sample(1:length(reverse_reads), size = 12)
random_samples

##  [1]  6  1 14 12 11  4  9 10 13  7  2  8

# Calculate and plot quality of these two samples
forward_filteredQual_plot_12 <- plotQualityProfile(forward_reads[random_samples]) + 
  labs(title = "Forward Read: Raw Quality")

reverse_filteredQual_plot_12 <- plotQualityProfile(reverse_reads[random_samples]) + 
  labs(title = "Reverse Read: Raw Quality")

# Plot them together with patchwork
forward_filteredQual_plot_12 + reverse_filteredQual_plot_12

Aggregated Raw Quality Plots

# Aggregate all QC plots 
# Forward reads
forward_preQC_plot <- 
  plotQualityProfile(forward_reads, aggregate = TRUE) + 
  labs(title = "Forward Pre-QC")

# reverse reads
reverse_preQC_plot <- 
  plotQualityProfile(reverse_reads, aggregate = TRUE) + 
  labs(title = "Reverse Pre-QC")

preQC_aggregate_plot <- 
  # Plot the forward and reverse together 
  forward_preQC_plot + reverse_preQC_plot

# Show the plot
preQC_aggregate_plot

Prepare a placeholder for filtered reads

# vector of our samples, extract the sample information from our file
samples <- scan("/local/workdir/cab565/git_repos/Alessandro/data/00_cutadapt/2011_samples.txt",
                what = "character")
#Intuition check
head(samples)

## [1] "SRR17060822" "SRR17060823" "SRR17060825" "SRR17060826" "SRR17060827"
## [6] "SRR17060828"

#place filtered reads into filtered_fastqs_path
filtered_fastqs_path <- "data/01_DADA2/02a_2011_filtered_fastqs"
filtered_fastqs_path

## [1] "data/01_DADA2/02a_2011_filtered_fastqs"

# create 2 variables : filtered_F, filtered_R
filtered_forward_reads <- 
  file.path(filtered_fastqs_path, paste0(samples, "_R1_filtered.fastq.gz"))

#Intuition check
head(filtered_forward_reads)

## [1] "data/01_DADA2/02a_2011_filtered_fastqs/SRR17060822_R1_filtered.fastq.gz"
## [2] "data/01_DADA2/02a_2011_filtered_fastqs/SRR17060823_R1_filtered.fastq.gz"
## [3] "data/01_DADA2/02a_2011_filtered_fastqs/SRR17060825_R1_filtered.fastq.gz"
## [4] "data/01_DADA2/02a_2011_filtered_fastqs/SRR17060826_R1_filtered.fastq.gz"
## [5] "data/01_DADA2/02a_2011_filtered_fastqs/SRR17060827_R1_filtered.fastq.gz"
## [6] "data/01_DADA2/02a_2011_filtered_fastqs/SRR17060828_R1_filtered.fastq.gz"

length(filtered_forward_reads)

## [1] 14

filtered_reverse_reads <- file.path(filtered_fastqs_path, paste0(samples,
                                                  "_R2_filtered.fastq.gz"))
#Intuition check
length(filtered_reverse_reads)

## [1] 14

Filter and Trim Reads

Parameters of filter and trim DEPEND ON THE DATASET

maxN = number of N bases. Remove all Ns from the data.
maxEE = quality filtering threshold applied to expected errors. By default, all expected errors. Mar recommends using c(1,1). Here, if there is maxEE expected errors, its okay. If more, throw away sequence.
trimLeft = trim certain number of base pairs on start of each read
truncQ = truncate reads at the first instance of a quality score less than or equal to selected number. Chose 2
rm.phix = remove phi x
compress = make filtered files .gzipped
multithread = multithread

#Assign a vector to filtered reads
#Trim out poor bases
#Write out filtered fastq files
filtered_reads <-
  filterAndTrim(fwd = forward_reads, filt = filtered_forward_reads,
              rev = reverse_reads, filt.rev = filtered_reverse_reads,
              trimLeft = c(17,20), truncLen = c(245,245), 
              maxN = 0, maxEE = c(1, 1),truncQ = 2, rm.phix = TRUE,
              compress = TRUE, multithread = 6)

# Primers are V3-V4 
# 341F (5′-CCT ACG GGN GGC WGC AG-3′)     (17 bp) from Herlemann et al. 2011
# 806R (5′-GGA CTA CHV GGG TWT CTA AT-3′) (20 bp) from Caporaso et al. 2011

Trimmed Quality Plots

# Plot the 12 random samples after QC
forward_filteredQual_plot_12 <- 
  plotQualityProfile(filtered_forward_reads[random_samples]) + 
  labs(title = "Trimmed Forward Read Quality")

reverse_filteredQual_plot_12 <- 
  plotQualityProfile(filtered_reverse_reads[random_samples]) + 
  labs(title = "Trimmed Reverse Read Quality")

# Put the two plots together 
forward_filteredQual_plot_12 + reverse_filteredQual_plot_12

Aggregated Trimmed Plots

# Aggregate all QC plots 
# Forward reads
forward_postQC_plot <- 
  plotQualityProfile(filtered_forward_reads, aggregate = TRUE) + 
  labs(title = "Forward Post-QC")

# reverse reads
reverse_postQC_plot <- 
  plotQualityProfile(filtered_reverse_reads, aggregate = TRUE) + 
  labs(title = "Reverse Post-QC")

postQC_aggregate_plot <- 
  # Plot the forward and reverse together 
  forward_postQC_plot + reverse_postQC_plot

# Show the plot
postQC_aggregate_plot

Stats on read output from `filterAndTrim`

#Make output into dataframe
filtered_df <- as.data.frame(filtered_reads)
head(filtered_df)

##                        reads.in reads.out
## SRR17060822_1.fastq.gz   132360     13736
## SRR17060823_1.fastq.gz   215557     15775
## SRR17060825_1.fastq.gz   116672     10677
## SRR17060826_1.fastq.gz   109215      7689
## SRR17060827_1.fastq.gz   101244      6053
## SRR17060828_1.fastq.gz   125327     11382

# calculate some stats
filtered_df %>%
  reframe(median_reads_in = median(reads.in),
          median_reads_out = median(reads.out),
          median_percent_retained = (median(reads.out)/median(reads.in)))

##   median_reads_in median_reads_out median_percent_retained
## 1          117704            10635              0.09035377

Enough reads are passing the filterAndTrim step. The quality could be a lot better, there are still reads with quality scores below 30. I don’t know how to be more stringent with the trimming and not trim off too much.

Error Modeling

Note every sequencing run needs to be run separately! The error model MUST be run separately on each illumina dataset. If you’d like to combine the datasets from multiple sequencing runs, you’ll need to do the exact same filterAndTrim() step AND, very importantly, you’ll need to have the same primer and ASV length expected by the output.

Infer error rates for all possible transitions within purines and pyrimidines (A<>G or C<>T) and transversions between all purine and pyrimidine combinations.

Error model is learned by alternating estimation of the error rates and inference of sample composition until they converge.

Starts with the assumption that the error rates are the maximum (takes the most abundant sequence (“center”) and assumes it’s the only sequence not caused by errors).
Compares the other sequences to the most abundant sequence.
Uses at most 10⁸ nucleotides for the error estimation.
Uses parametric error estimation function of loess fit on the observed error rates.

#Forward reads
error_forward_reads <-
  learnErrors(filtered_forward_reads, multithread = 6)

## 28086180 total bases in 123185 reads from 14 samples will be used for learning the error rates.

#Plot forward reads errors
forward_error_plot <-
  plotErrors(error_forward_reads, nominalQ = TRUE) + 
  labs(title = "Forward Read Error Model")

#Reverse reads
error_reverse_reads <-
  learnErrors(filtered_reverse_reads, multithread = 6)

## 27716625 total bases in 123185 reads from 14 samples will be used for learning the error rates.

#Plot reverse reads errors
reverse_error_plot <-
  plotErrors(error_reverse_reads, nominalQ = TRUE) +
    labs(title = "Reverse Read Error Model")

#Put the two plots together
forward_error_plot + reverse_error_plot

## Warning in scale_y_log10(): log-10 transformation introduced infinite values.
## log-10 transformation introduced infinite values.
## log-10 transformation introduced infinite values.
## log-10 transformation introduced infinite values.

Points do a good job following the black lines. There are a few plots with points off the black lines.

The error rates for each possible transition (A→C, A→G, …) are shown in the plot above.

Details of the plot: - Points: The observed error rates for each consensus quality score.
- Black line: Estimated error rates after convergence of the machine-learning algorithm.
- Red line: The error rates expected under the nominal definition of the Q-score.

Similar to what is mentioned in the dada2 tutorial: the estimated error rates (black line) are a “reasonably good” fit to the observed rates (points), and the error rates drop with increased quality as expected. We can now infer ASVs!

Infer ASVs

An important note: This process occurs separately on forward and reverse reads! This is quite a different approach from how OTUs are identified in Mothur and also from UCHIME, oligotyping, and other OTU, MED, and ASV approaches.

#Infer forward ASVs
dada_forward <- dada(filtered_forward_reads, 
                     err = error_forward_reads,
                     multithread = 6)

## Sample 1 - 13736 reads in 4550 unique sequences.
## Sample 2 - 15775 reads in 6869 unique sequences.
## Sample 3 - 10677 reads in 4311 unique sequences.
## Sample 4 - 7689 reads in 3224 unique sequences.
## Sample 5 - 6053 reads in 2252 unique sequences.
## Sample 6 - 11382 reads in 4091 unique sequences.
## Sample 7 - 336 reads in 263 unique sequences.
## Sample 8 - 366 reads in 188 unique sequences.
## Sample 9 - 14381 reads in 4119 unique sequences.
## Sample 10 - 11683 reads in 2903 unique sequences.
## Sample 11 - 11345 reads in 3189 unique sequences.
## Sample 12 - 10593 reads in 3921 unique sequences.
## Sample 13 - 8700 reads in 4077 unique sequences.
## Sample 14 - 469 reads in 230 unique sequences.

#Infer reverse ASVs
dada_reverse <- dada(filtered_reverse_reads, 
                     err = error_reverse_reads, 
                     multithread = 6)

## Sample 1 - 13736 reads in 4368 unique sequences.
## Sample 2 - 15775 reads in 7063 unique sequences.
## Sample 3 - 10677 reads in 4128 unique sequences.
## Sample 4 - 7689 reads in 3126 unique sequences.
## Sample 5 - 6053 reads in 2076 unique sequences.
## Sample 6 - 11382 reads in 4214 unique sequences.
## Sample 7 - 336 reads in 254 unique sequences.
## Sample 8 - 366 reads in 199 unique sequences.
## Sample 9 - 14381 reads in 4730 unique sequences.
## Sample 10 - 11683 reads in 3261 unique sequences.
## Sample 11 - 11345 reads in 2706 unique sequences.
## Sample 12 - 10593 reads in 3383 unique sequences.
## Sample 13 - 8700 reads in 4297 unique sequences.
## Sample 14 - 469 reads in 251 unique sequences.

#Inspect
dada_forward[1]

## $SRR17060822_R1_filtered.fastq.gz
## dada-class: object describing DADA2 denoising results
## 89 sequence variants were inferred from 4550 input unique sequences.
## Key parameters: OMEGA_A = 1e-40, OMEGA_C = 1e-40, BAND_SIZE = 16

dada_reverse[1]

## $SRR17060822_R2_filtered.fastq.gz
## dada-class: object describing DADA2 denoising results
## 76 sequence variants were inferred from 4368 input unique sequences.
## Key parameters: OMEGA_A = 1e-40, OMEGA_C = 1e-40, BAND_SIZE = 16

dada_forward[12]

## $SRR17060843_R1_filtered.fastq.gz
## dada-class: object describing DADA2 denoising results
## 74 sequence variants were inferred from 3921 input unique sequences.
## Key parameters: OMEGA_A = 1e-40, OMEGA_C = 1e-40, BAND_SIZE = 16

dada_reverse[12]

## $SRR17060843_R2_filtered.fastq.gz
## dada-class: object describing DADA2 denoising results
## 58 sequence variants were inferred from 3383 input unique sequences.
## Key parameters: OMEGA_A = 1e-40, OMEGA_C = 1e-40, BAND_SIZE = 16

Merge Forward and Reverse ASVs

Now, merge the forward and reverse ASVs into contigs.

# merge forward and reverse ASVs
merged_ASVs <- mergePairs(dada_forward, filtered_forward_reads, 
                          dada_reverse, filtered_reverse_reads,
                          verbose = TRUE)

## 12652 paired-reads (in 102 unique pairings) successfully merged out of 13509 (in 365 pairings) input.

## 13430 paired-reads (in 259 unique pairings) successfully merged out of 15075 (in 815 pairings) input.

## 9088 paired-reads (in 113 unique pairings) successfully merged out of 10237 (in 555 pairings) input.

## 6636 paired-reads (in 112 unique pairings) successfully merged out of 7210 (in 338 pairings) input.

## 5408 paired-reads (in 45 unique pairings) successfully merged out of 5895 (in 166 pairings) input.

## 10362 paired-reads (in 115 unique pairings) successfully merged out of 11112 (in 330 pairings) input.

## 165 paired-reads (in 12 unique pairings) successfully merged out of 212 (in 15 pairings) input.

## 305 paired-reads (in 11 unique pairings) successfully merged out of 316 (in 13 pairings) input.

## 13098 paired-reads (in 100 unique pairings) successfully merged out of 13937 (in 430 pairings) input.

## 11237 paired-reads (in 53 unique pairings) successfully merged out of 11498 (in 95 pairings) input.

## 10882 paired-reads (in 68 unique pairings) successfully merged out of 11211 (in 156 pairings) input.

## 9318 paired-reads (in 86 unique pairings) successfully merged out of 10259 (in 355 pairings) input.

## 6763 paired-reads (in 201 unique pairings) successfully merged out of 7976 (in 832 pairings) input.

## 415 paired-reads (in 11 unique pairings) successfully merged out of 427 (in 15 pairings) input.

# Evaluate the output 
typeof(merged_ASVs)

## [1] "list"

length(merged_ASVs)

## [1] 14

names(merged_ASVs)

##  [1] "SRR17060822_R1_filtered.fastq.gz" "SRR17060823_R1_filtered.fastq.gz"
##  [3] "SRR17060825_R1_filtered.fastq.gz" "SRR17060826_R1_filtered.fastq.gz"
##  [5] "SRR17060827_R1_filtered.fastq.gz" "SRR17060828_R1_filtered.fastq.gz"
##  [7] "SRR17060829_R1_filtered.fastq.gz" "SRR17060839_R1_filtered.fastq.gz"
##  [9] "SRR17060840_R1_filtered.fastq.gz" "SRR17060841_R1_filtered.fastq.gz"
## [11] "SRR17060842_R1_filtered.fastq.gz" "SRR17060843_R1_filtered.fastq.gz"
## [13] "SRR17060844_R1_filtered.fastq.gz" "SRR17060845_R1_filtered.fastq.gz"

# Inspect the merger data.frame from the 20210602-MA-ABB1P 
#head(merged_ASVs[[3]])

Create Raw ASV Count Table

# Create the ASV Count Table 
raw_ASV_table <- makeSequenceTable(merged_ASVs)

# Write out the file to data/01_DADA2


# Check the type and dimensions of the data
dim(raw_ASV_table)

## [1]  14 847

class(raw_ASV_table)

## [1] "matrix" "array"

typeof(raw_ASV_table)

## [1] "integer"

# Inspect the distribution of sequence lengths of all ASVs in dataset 
table(nchar(getSequences(raw_ASV_table)))

## 
## 228 229 366 367 368 369 370 371 375 377 386 387 388 391 392 393 
##   8   5  27 104  29  33 137  17   4   2   2 264   2  52 146  15

# Inspect the distribution of sequence lengths of all ASVs in dataset 
# AFTER TRIM
data.frame(Seq_Length = nchar(getSequences(raw_ASV_table))) %>%
  ggplot(aes(x = Seq_Length )) + 
  geom_histogram() + 
  labs(title = "Raw distribution of ASV length")

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

###################################################
###################################################
# TRIM THE ASVS
# Let's trim the ASVs to only be the right size, which is 249.
# 249 originates from our expected amplicon of 252 - 3bp in the forward read due to low quality.

# We will allow for a few 
raw_ASV_table_trimmed <- raw_ASV_table[,nchar(colnames(raw_ASV_table)) %in% 387:393]

# Inspect the distribution of sequence lengths of all ASVs in dataset 
table(nchar(getSequences(raw_ASV_table_trimmed)))

## 
## 387 388 391 392 393 
## 264   2  52 146  15

# What proportion is left of the sequences? 
sum(raw_ASV_table_trimmed)/sum(raw_ASV_table)

## [1] 0.7019743

# Inspect the distribution of sequence lengths of all ASVs in dataset 
# AFTER TRIM
data.frame(Seq_Length = nchar(getSequences(raw_ASV_table_trimmed))) %>%
  ggplot(aes(x = Seq_Length )) + 
  geom_histogram() + 
  labs(title = "Trimmed distribution of ASV length")

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

# Note the peak at 249 is ABOVE 3000

# Let's zoom in on the plot 
data.frame(Seq_Length = nchar(getSequences(raw_ASV_table_trimmed))) %>%
  ggplot(aes(x = Seq_Length )) + 
  geom_histogram() + 
  labs(title = "Trimmed distribution of ASV length") + 
  scale_y_continuous(limits = c(0, 500))

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Taking into account the lower, zoomed-in plot. Do we want to remove those extra ASVs?

Remove Chimeras

Sometimes chimeras arise in our workflow.

Chimeric sequences are artificial sequences formed by the combination of two or more distinct biological sequences. These chimeric sequences can arise during the polymerase chain reaction (PCR) amplification step of the 16S rRNA gene, where fragments from different templates can be erroneously joined together.

Chimera removal is an essential step in the analysis of 16S sequencing data to improve the accuracy of downstream analyses, such as taxonomic assignment and diversity assessment. It helps to avoid the inclusion of misleading or spurious sequences that could lead to incorrect biological interpretations.

# Remove the chimeras in the raw ASV table
noChimeras_ASV_table <- removeBimeraDenovo(raw_ASV_table_trimmed, 
                                           method="consensus", 
                                           multithread=6, verbose=TRUE)

## Identified 193 bimeras out of 479 input sequences.

# Check the dimensions
dim(noChimeras_ASV_table)

## [1]  14 286

# What proportion is left of the sequences? 
sum(noChimeras_ASV_table)/sum(raw_ASV_table_trimmed)

## [1] 0.9632307

sum(noChimeras_ASV_table)/sum(raw_ASV_table)

## [1] 0.6761632

# Plot it 
data.frame(Seq_Length_NoChim = nchar(getSequences(noChimeras_ASV_table))) %>%
  ggplot(aes(x = Seq_Length_NoChim )) + 
  geom_histogram()+ 
  labs(title = "Trimmed + Chimera Removal distribution of ASV length")

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Track the read counts

Here, we will look at the number of reads that were lost in the filtering, denoising, merging, and chimera removal.

# A little function to identify number seqs 
getN <- function(x) sum(getUniques(x))

# Make the table to track the seqs 
track <- cbind(filtered_reads, 
               sapply(dada_forward, getN),
               sapply(dada_reverse, getN),
               sapply(merged_ASVs, getN),
               rowSums(noChimeras_ASV_table))

head(track)

##                        reads.in reads.out                       
## SRR17060822_1.fastq.gz   132360     13736 13571 13660 12652 7710
## SRR17060823_1.fastq.gz   215557     15775 15314 15495 13430 8523
## SRR17060825_1.fastq.gz   116672     10677 10320 10565  9088 5997
## SRR17060826_1.fastq.gz   109215      7689  7334  7470  6636 4301
## SRR17060827_1.fastq.gz   101244      6053  5934  5985  5408 1931
## SRR17060828_1.fastq.gz   125327     11382 11215 11240 10362 5755

# Update column names to be more informative (most are missing at the moment!)
colnames(track) <- c("input", "filtered", "denoisedF", "denoisedR", "merged", "nochim")
rownames(track) <- samples

# Generate a dataframe to track the reads through our DADA2 pipeline
track_counts_df <- 
  track %>%
  # make it a dataframe
  as.data.frame() %>%
  rownames_to_column(var = "names") %>%
  mutate(perc_reads_retained = 100 * nochim / input)

# Visualize it in table format 
DT::datatable(track_counts_df)

# Plot it!
track_counts_df %>%
  pivot_longer(input:nochim, names_to = "read_type", values_to = "num_reads") %>%
  mutate(read_type = fct_relevel(read_type, 
                                 "input", "filtered", "denoisedF", "denoisedR", "merged", "nochim")) %>%
  ggplot(aes(x = read_type, y = num_reads, fill = read_type)) + 
  geom_line(aes(group = names), color = "grey") + 
  geom_point(shape = 21, size = 3, alpha = 0.8) + 
  scale_fill_brewer(palette = "Spectral") + 
  labs(x = "Filtering Step", y = "Number of Sequences") + 
  theme_bw()

Prepare the data for export!

1. ASV Table

Below, we will prepare the following:

Two ASV Count tables:
1. With ASV seqs: ASV headers include the entire ASV sequence ~250bps.
2. with ASV names: This includes re-written and shortened headers like ASV_1, ASV_2, etc, which will match the names in our fasta file below.
ASV_fastas: A fasta file that we can use to build a tree for phylogenetic analyses (e.g. phylogenetic alpha diversity metrics or UNIFRAC dissimilarty).

Finalize ASV Count Tables

########### 2. COUNT TABLE ###############
############## Modify the ASV names and then save a fasta file!  ############## 
# Give headers more manageable names
# First pull the ASV sequences
asv_seqs <- colnames(noChimeras_ASV_table)
asv_seqs[1:5]

## [1] "TGGGCGCAAGCCTGATGCAGCCATGCCGCGTGTGTGAAGAAGGCCTTCGGGTTGTAAAGCACTTTCAGCAGTGAGGAAGGTAATGTAGTTAATACCTGCATTATTTGACGTTAGCTGCAGAAGAAGCACCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAGGGTGCGAGCGTTAATCGGAATTACTGGGCGTAAAGCGCATGCAGGCGGTTTGTTAAGCAAGATGTGAAAGCCCCGGGCTTAACCTGGGAATCGCATTTTGAACTGGCAAGCTAGAGTCTTGTAGAGGGGGGTAGAATTTCAGGTGTAGCGGTGAAATGCGTAGAGATCTGAAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGATGCGA"
## [2] "TGGACGCAAGTCTGAACCAGCCAAGTCGCGTGAAGGATGAAGGTCTTATGGATTGTAAACTTCTTTTATATGGGAATAAAAAAGGTCACGTGTGGCCTATTGCATGTACCATATGAATAAGGATCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAGGATCCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGTCTCTTAAGTCAGCGTTGAAAGTTTTCGGCTCAACCGGAAAATTGGCATTGAAACTGGGAGACTTGAGTGTAAATGAAGTTGGCGGAATTCGTTGTGTAGCGGTGAAATGCATAGATATAACGAAGAACTCCGATTGCGAAGGCAGCTGACTAACATACAACTGACGCTGAGGCACGA"     
## [3] "TGGACGCAAGTCTGAACCAGCCAAGTCGCGTGAAGGATGAAGGTCTTATGGATTGTAAACTTCTTTTATATGGGAATAAAAAAGGTCACGTGTGATCTATTGCATGTACCATATGAATAAGGATCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAGGATCCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGTCTCTTAAGTCAGCGTTGAAAGTTTTCGGCTCAACCGGAAAATTGGCATTGAAACTGGGAGACTTGAGTGTAAATGAAGTTGGCGGAATTCGTTGTGTAGCGGTGAAATGCATAGATATAACGAAGAACTCCGATTGCGAAGGCAGCTGACTAACATACAACTGACGCTGAGGCACGA"     
## [4] "TGGGCGCAAGCCTGATGCAGCCATGCCGCGTGTGTGAAGAAGGCCTTCGGGTTGTAAAGCACTTTCAGCAGTGAGGAAGGTAATGTAGTTAATACCTGCATTATTTGACGTTAGCTGCAGAAGAAGCACCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAGGGTGCGAGCGTTAATCGGAATTACTGGGCGTAAAGCGCATGCAGGCGGTTTGTTAAGCAAGATGTGAAAGCCCCGGGCTTAACCTGGGAATTGCATTTTGAACTGGCAAGCTAGAGTCTTGTAGAGGGGGGTAGAATTTCAGGTGTAGCGGTGAAATGCGTAGAGATCTGAAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGATGCGA"
## [5] "TGGGGGAAACCCTGACGGAGCGACACTGCGTGAATGATGAAGGCCTTCGGGTTGTAAAGTTCTTTTATAAAGGAAGAATAAGTTGGGTAGGAAATGACCTGATGATGACGGTACTTTATGAATAAGTCCCGGCTAATTACGTGCCAGCAGCCGCGGTAATACGTAAGGGACGAGCGTTATTCGGAATTACTGGGCGTAAAGGGCGTGTAGGCGGTTAATTAGGCTGAGTGTTAAAGACTGGGGCTCAACTCCAGAAGGGCATTCAGAACCGGTTGACTAGAATCTGGTGGAAGGCAACGGAATTTCCCGTGTAGCGGTGGAATGCATAGATATGGGAAGGAACACCAAAGGCGAAGGCAGTTGTCTATGCCAAGATTGACGCTGAGGCGCGA"

# make headers for our ASV seq fasta file, which will be our asv names
asv_headers <- vector(dim(noChimeras_ASV_table)[2], mode = "character")
asv_headers[1:5]

## [1] "" "" "" "" ""

# loop through vector and fill it in with ASV names 
for (i in 1:dim(noChimeras_ASV_table)[2]) {
  asv_headers[i] <- paste(">ASV", i, sep = "_")
}

# intitution check
asv_headers[1:5]

## [1] ">ASV_1" ">ASV_2" ">ASV_3" ">ASV_4" ">ASV_5"

##### Rename ASVs in table then write out our ASV fasta file! 
#View(noChimeras_ASV_table)
asv_tab <- t(noChimeras_ASV_table)
#View(asv_tab)

## Rename our asvs! 
row.names(asv_tab) <- sub(">", "", asv_headers)
#View(asv_tab)

Write `01_DADA2` files

Now, we will write the files! We will write the following to the data/01_DADA2/05_2011_analysis folder. We will save both as files that could be submitted as supplements AND as .RData objects for easy loading into the next steps into R.:

ASV_counts.tsv: ASV count table that has ASV names that are re-written and shortened headers like ASV_1, ASV_2, etc, which will match the names in our fasta file below. This will also be saved as data/01_DADA2/05_2011_analysis/ASV_counts.RData.
ASV_counts_withSeqNames.tsv: This is generated with the data object in this file known as noChimeras_ASV_table. ASV headers include the entire ASV sequence ~250bps. In addition, we will save this as a .RData object as data/01_DADA2/05_2011_analysis/noChimeras_ASV_table.RData as we will use this data in analysis/02_PreProcessing.Rmd to assign the taxonomy from the sequence headers.
ASVs.fasta: A fasta file output of the ASV names from ASV_counts.tsv and the sequences from the ASVs in ASV_counts_withSeqNames.tsv. A fasta file that we can use to build a tree for phylogenetic analyses (e.g. phylogenetic alpha diversity metrics or UNIFRAC dissimilarty).
We will also make a copy of ASVs.fasta in data/02_PreProcessing/ to be used for the taxonomy classification in the next step in the workflow.
Write out the taxonomy table
track_read_counts.RData: To track how many reads we lost throughout our workflow that could be used and plotted later. We will add this to the metadata in analysis/02_PreProcessing.Rmd.

# FIRST, we will save our output as regular files, which will be useful later on. 
# Save to regular .tsv file 
# Write BOTH the modified and unmodified ASV tables to a file!
# Write count table with ASV numbered names (e.g. ASV_1, ASV_2, etc)
write.table(asv_tab, "data/01_DADA2/Alessandro_2011/ASV_counts.tsv", sep = "\t", quote = FALSE, col.names = NA)
# Write count table with ASV sequence names
write.table(noChimeras_ASV_table, "data/01_DADA2/Alessandro_2011/ASV_counts_withSeqNames.tsv", sep = "\t", quote = FALSE, col.names = NA)
# Write out the fasta file for reference later on for what seq matches what ASV
asv_fasta <- c(rbind(asv_headers, asv_seqs))
# Save to a file!
write(asv_fasta, "data/01_DADA2/Alessandro_2011/ASVs.fasta")

# SECOND, let's save to a RData object 
# Each of these files will be used in the analysis/02_Taxonomic_Assignment
# RData objects are for easy loading :) 
save(noChimeras_ASV_table, file = "data/01_DADA2/Alessandro_2011/noChimeras_ASV_table.RData")
save(asv_tab, file = "data/01_DADA2/Alessandro_2011/ASV_counts.RData")
# And save the track_counts_df a R object, which we will merge with metadata information in the next step of the analysis in nalysis/02_Taxonomic_Assignment. 
save(track_counts_df, file = "data/01_DADA2/Alessandro_2011/track_read_counts.RData")

##Session information

#Ensure reproducibility
devtools::session_info()

## ─ Session info ───────────────────────────────────────────────────────────────
##  setting  value
##  version  R version 4.3.2 (2023-10-31)
##  os       Rocky Linux 9.0 (Blue Onyx)
##  system   x86_64, linux-gnu
##  ui       X11
##  language (EN)
##  collate  en_US.UTF-8
##  ctype    en_US.UTF-8
##  tz       America/New_York
##  date     2024-04-25
##  pandoc   3.1.1 @ /usr/lib/rstudio-server/bin/quarto/bin/tools/ (via rmarkdown)
## 
## ─ Packages ───────────────────────────────────────────────────────────────────
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##  ade4                   1.7-22     2023-02-06 [1] CRAN (R 4.3.2)
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## ──────────────────────────────────────────────────────────────────────────────

Infer ASVs with DADA2 - 2011 samples

Calla Bush St George

2024-04-25

Goals for this file

Output that we need:

Before you start

Set my seed

Load Libraries

Load Data

Raw Quality plots

Aggregated Raw Quality Plots

Prepare a placeholder for filtered reads

Filter and Trim Reads

Trimmed Quality Plots

Aggregated Trimmed Plots

Stats on read output from `filterAndTrim`

Error Modeling

Infer ASVs

Merge Forward and Reverse ASVs

Create Raw ASV Count Table

Remove Chimeras

Track the read counts

Prepare the data for export!

1. ASV Table

Finalize ASV Count Tables

Write `01_DADA2` files

Infer ASVs with DADA2 - 2011 samples

Calla Bush St George

2024-04-25

Goals for this file

Output that we need:

Before you start

Set my seed

Load Libraries

Load Data

Raw Quality plots

Aggregated Raw Quality Plots

Prepare a placeholder for filtered reads

Filter and Trim Reads

Trimmed Quality Plots

Aggregated Trimmed Plots

Stats on read output from filterAndTrim

Error Modeling

Infer ASVs

Merge Forward and Reverse ASVs

Create Raw ASV Count Table

Remove Chimeras

Track the read counts

Prepare the data for export!

1. ASV Table

Finalize ASV Count Tables

Write 01_DADA2 files

Stats on read output from `filterAndTrim`

Write `01_DADA2` files